EXPERIMENTATION. HARRISON CARRANZA School of Computer Science and Mathematics

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1 BIG DATA WITH HADOOP EXPERIMENTATION HARRISON CARRANZA Marist College APARICIO CARRANZA NYC College of Technology CUNY ECC Conference 2016 Poughkeepsie, NY, June 12-14, 2016 Marist College

2 AGENDA Contents WHAT IS AND WHY USE HADOOP? HISTORY AND WHO USES HADOOP KEY HADOOP TECHNOLOGIES HADOOP STRUCTURE RESEARCH AND IMPLEMENTATION CONCLUSIONS

3 Contents WHAT IS AND WHY USE HADOOP? Open Source Apache Project Hadoop Core includes: Distributed File System - distributes data Map/Reduce - distributes application Written in Java Runs on Linux, Mac OS/X, Windows

4 Contents WHAT IS AND WHY USE HADOOP? How do you scale up applications? Run jobs processing hundreds of terabytes of data Takes 11 days to read on 1 computer Need lots of cheap computers Fixes speed problem (15 minutes on 1000 computers) Reliability problems In large clusters, computers fail every day Cluster size is not fixed Information on one computer gets lost Need common infrastructure Must be efficient and reliable

5 Contents HISTORY AND WHO USES HADOOP Yahoo! became the primary contributor in 2006 It increased over the next decade up until now & is expected to grow beyond 2018

6 Contents HISTORY AND WHO USES HADOOP Yahoo! deployed large scale science clusters in 2007 Tons of Yahoo! Research papers emerge: WWW CIKM SIGIR VLDB Yahoo! began running major production jobs in Q1 2008

7 Contents HISTORY AND WHO USES HADOOP Amazon/A9 AOL Facebook Fox interactive media Google IBM New York Times PowerSet (now Microsoft) Quantcast Rackspace/Mailtrust Veoh Yahoo! New York Times More at

8 Contents HISTORY AND WHO USES HADOOP When you visit Facebook, you are interacting with data processed with Hadoop!

9 Contents HISTORY AND WHO USES HADOOP Content Optimization Search Index When you visit Facebook, you are interacting with data processed with Hadoop! Ads Optimization Content Feed Processing

10 Contents HISTORY AND WHO USES HADOOP NOWADAYS Facebook! has ~20,000 machines running Hadoop The largest clusters are currently 2000 nodes Several petabytes of user data (compressed, unreplicated) Facebook runs hundreds of thousands of jobs every month

11 Contents KEY HADOOP TECHNOLOGIES Hadoop Core Distributed File System MapReduce Framework Pig (initiated by Yahoo!) Parallel Programming Language and Runtime Hbase (initiated by Powerset) Table storage for semi-structured data Zookeeper (initiated by Yahoo!) Coordinating distributed systems Hive (initiated by Facebook) SQL-like query language and metastore

12 Contents HADOOP DISTRIBUTED FILE SYSTEM (HDFS) HDFS is designed to reliably store very large files across machines in a large cluster It is inspired by the Google File System Hadoop DFS stores each file as a sequence of blocks, all blocks in a file except the last block are the same size Blocks belonging to a file are replicated for fault tolerance The block size and replication factor are configurable per file Files in HDFS are "write once" and have strictly one writer at any time Hadoop Distributed File System Goals: Store large data sets Cope with hardware failure Emphasize streaming data access

13 HADOOP Contents STRUCTURE Commodity hardware Linux PCs with local 4 disks Typically in 2 level architecture 40 nodes/rack Uplink from rack is 8 gigabit Rack-internal is 1 gigabit all-to-all

14 HADOOP Contents STRUCTURE Single namespace for entire cluster Managed by a single namenode Files are single-writer and append-only Optimized for streaming reads of large files Files are broken in to large block Typically 128 MB Replicated to several datanodes, for reliability Client talks to both namenode and datanodes Data is not sent through the namenode Throughput of file system scales nearly linearly with the number of nodes Access from Java, C, or command line

15 HADOOP Contents STRUCTURE Java and C++ APIs In Java use Objects, while in C++ bytes Each task can process data sets larger than RAM Automatic re-execution on failure In a large cluster, some nodes are always slow or flaky Framework re-executes failed tasks Locality optimizations Map-Reduce queries HDFS for locations of input data Map tasks are scheduled close to the inputs when possible

16 Contents RESEARCH & IMPLEMENTATION There were three different multi node clusters produced each utilizing: Microsoft Windows 7 Linux Ubuntu Mac OS X Yosemite Hadoop functions were then compared in each of these three different environments The data that was used in this experiment were from Wikipedia s data dump site

17 Contents RESEARCH & IMPLEMENTATION

18 Contents RESEARCH & IMPLEMENTATION

19 Contents RESEARCH & IMPLEMENTATION

20 Contents RESEARCH & IMPLEMENTATION

21 Contents RESEARCH & IMPLEMENTATION

22 Contents RESEARCH & IMPLEMENTATION

23 CONCLUSIONS Contents When name node memory usage is higher than 75%, reading and writing HDFS files through Hadoop API shall fail; caused by Java Hashmap Name node s QPS (query per second) can be optimized by fine-tuning the handler counts higher than the number of tasks that are run in parallel The memory available affects the namenode s performance in each OS platform; therefore allocation of memory is essential Since memory has such an effect performance, the operating system utilizes less RAM for background and utility features to yield better results Ubuntu Linux would be the ideal environment since it uses the least amount of memory, followed by Mac OSX and then finally Windows

24 THANK YOU!!!

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